The use of vibration as a training intervention has been suggested for more than a decade. Following the initial promising studies, a large number of investigations have been conducted to understand the acute and chronic effects of this novel training modality mainly using special populations, sedentary, physically active, and aged individuals. There is a small number of studies involving athletes. For this reason it is at the moment very difficult to provide safe and effective training guidelines to athletes. We discuss the current findings related to the effectiveness on elite athletes and provide some guidance on practical applications. Vibration is without a doubt an interesting intervention; however, more needs to be done to understand the physiological mechanisms involved in the adaptive responses to vibration exercise. Furthermore, more studies are needed to establish a dose-response relationship to vibration training to provide indications on safe and effective vibration training prescriptions.
Marco Cardinale and Julie A. Erskine
Marco Cardinale and Matthew C. Varley
The need to quantify aspects of training to improve training prescription has been the holy grail of sport scientists and coaches for many years. Recently, there has been an increase in scientific interest, possibly due to technological advancements and better equipment to quantify training activities. Over the last few years there has been an increase in the number of studies assessing training load in various athletic cohorts with a bias toward subjective reports and/or quantifications of external load. There is an evident lack of extensive longitudinal studies employing objective internal-load measurements, possibly due to the cost-effectiveness and invasiveness of measures necessary to quantify objective internal loads. Advances in technology might help in developing better wearable tools able to ease the difficulties and costs associated with conducting longitudinal observational studies in athletic cohorts and possibly provide better information on the biological implications of specific external-load patterns. Considering the recent technological developments for monitoring training load and the extensive use of various tools for research and applied work, the aim of this work was to review applications, challenges, and opportunities of various wearable technologies.
Gennaro Boccia, Marco Cardinale, and Paolo Riccardo Brustio
Purpose: This study investigated (1) the transition rate of elite world-class throwers, (2) the age of peak performance in either elite junior and/or elite senior athletes, and (3) if relative age effect (RAE) influences the chance of being considered elite in junior and/or senior category. Methods: The career performance trajectories of 5108 throwers (49.9% females) were extracted from the World Athletics database. The authors identified throwers who had reached the elite level (operationally defined as the World all-time top 50 ranked for each age category) in either junior and/or senior category and calculated the junior-to-senior transition rate. The age of peak performance and the RAE were also investigated. Results: The transition rate at 16 and 18 years of age was 6% and 12% in males and 16% and 24% in females, respectively. Furthermore, elite senior throwers reached their personal best later in life than elite junior throwers. The athletes of both genders considered elite in the junior category showed a large RAE. Interestingly, male athletes who reached the elite level in senior category also showed appreciable RAE. Conclusions: Only a few of the athletes who reach the top 50 in the world at 16 or 18 years of age manage to become elite senior athletes, underlining that success at the beginning of an athletic career does not predict success in the athlete’s senior career. Moreover, data suggest that being relatively older may confer a benefit across the whole career of male throwers.
Gennaro Boccia, Marco Cardinale, and Paolo Riccardo Brustio
Purpose: To quantify how many of the top 50 under-18 (U18) sprinters in the world managed to become top 50 ranked as adult competitors. The authors also described the career trajectory of athletes ranked in the top 50 during either U18 or senior category. Methods: A total of 4924 male and female athletes competing in sprint races and ranked in the International Association of Athletics Federations (now World Athletics) lists in any of the seasons between the 2000 and 2018 were included in the study. The athletes ranked in the top 50 positions of all-time lists during U18, senior, or both categories were analyzed. Results: Only 17% of the male and 21% of the female top 50 ranked U18 managed to become top 50 ranked senior athletes. The top 50 ranked senior athletes consistently produced yearly larger improvements during late adolescence and early adulthood compared with those who ranked in the top 50 at U18. Furthermore, top 50 ranked senior athletes reached their peak performance later compared with the top 50 ranked only in U18. Conclusions: This study confirms that early success in track and field is not a good predictor of success at senior level in sprinting events. The yearly performance improvements and their tracking provide the most suitable approach to identify athletes more likely to succeed as elite performers in adulthood. The authors hope that the results of this study can provide useful comparative data and reference criteria for talent-identification and -development programs.
Pitre C. Bourdon, Marco Cardinale, Warren Gregson, and N. Timothy Cable
Andy Galbraith, James Hopker, Marco Cardinale, Brian Cunniffe, and Louis Passfield
To examine the training and concomitant changes in laboratory- and field-test performance of highly trained endurance runners.
Fourteen highly trained male endurance runners (mean ± SD maximal oxygen uptake [VO2max] 69.8 ± 6.3 mL · kg−1 · min−1) completed this 1-y training study commencing in April. During the study the runners undertook 5 laboratory tests of VO2max, lactate threshold (LT), and running economy and 9 field tests to determine critical speed (CS) and the modeled maximum distance performed above CS (D′). The data for different periods of the year were compared using repeated-measures ANOVA. The influence of training on laboratory- and field-test changes was analyzed by multiple regression.
Total training distance varied during the year and was lower in May–July (333 ± 206 km, P = .01) and July–August (339 ± 206 km, P = .02) than in the subsequent January–February period (474 ± 188 km). VO2max increased from the April baseline (4.7 ± 0.4 L/min) in October and January periods (5.0 ± 0.4 L/min, P ≤ .01). Other laboratory measures did not change. Runners’ CS was lowest in August (4.90 ± 0.32 m/s) and highest in February (4.99 ± 0.30 m/s, P = .02). Total training distance and the percentage of training time spent above LT velocity explained 33% of the variation in CS.
Highly trained endurance runners achieve small but significant changes in VO2max and CS in a year. Increases in training distance and time above LT velocity were related to increases in CS.
Marco Cardinale, Rodney Whiteley, Ahmed Abdelrahman Hosny, and Nebojsa Popovic
Handball is an Olympic sport played indoors by 6 court players and 1 goalkeeper with rolling substitutions. Limited data exist on elite players competing in a world championship, and virtually no information exists on the evolution of time–motion performance over the course of a long tournament.
To analyze time–motion characteristics of elite male handball players of the last world championships, played in Qatar in 2015.
384 handball players from 24 national teams.
The athletes were analyzed during 88 matches using a tracking camera system and bespoke software (Prozone Handball v. 1.2, Prozone, Leeds, UK).
The average time on court (N = 2505) during the world championships for all players was 36:48 ± 20:27 min. Goalkeepers and left and right wings were on court most of the playing time (GK 43.00 ± 25:59 min; LW 42:02 ± 21:07 min; RW 43:44 ± 21:37 min). The total distance covered during each game (2607.5 ± 1438.4 m) consisted mostly of walking and jogging. The cumulative distance covered during the tournament was 16,313 ± 9423.3 m. Players performed 857.2 ± 445.7 activity changes with a recovery time of 124.3 ± 143 s. The average running pace was 78.2 ± 10.8 m/min. There was no significant difference between high-ranked and lower-ranked teams in terms of distance covered in different locomotion categories.
Specific physical conditioning is necessary to maximize performance of handball players and minimize the occurrence of fatigue when performing in long tournaments.
Thomas W. Jones, Barry C. Shillabeer, and Marco Cardinale
Context: The application of infrared thermography to assess the effects of athletic training is increasing. It is not known if changes in skin temperature (Tsk) as assessed by infrared thermography are affected by the training load or the muscle soreness experienced by the athlete. Purpose : To describe the variations in Tsk in body areas affected by running training and examine any relationships with subjective ratings of muscle soreness. The secondary aim was to assess the feasibility of using infrared thermography for assessing training load in 2 junior male middle-distance athletes. Methods: Data were collected over a 42-d observational period with Tsk of the quadriceps, knees, shins, lateral hamstrings, biceps femoris, and Achilles tendons, and the subjective ratings of muscle soreness were taken each morning prior to any training. All training load was quantified through heart rate, running speed, and distance covered. Changes in Tsk outside the typical error were identified. Relationships between Tsk and subjective ratings of muscle soreness were also examined. Results: Over the 42-d observational period, mean Tsk of the regions of interest was reported outside the typical error on day 31 and day 22 for athletes 1 and 2, respectively. These changes in Tsk did not follow trends similar to those of to training loadings. No significant relationships were observed between Tsk of any regions of interest and muscle soreness. Conclusions: Although Tsk changed outside the typical error throughout the 42-d observational period, these changes were not reflective of training load quantified through cardiovascular strain or subjective ratings of muscle soreness.
Brian Cunniffe, Kevin A. Morgan, Julien S. Baker, Marco Cardinale, and Bruce Davies
This study evaluated the effect of game venue and starting status on precompetitive psychophysiological measures in elite rugby union. Saliva samples were taken from players (starting XV, n = 15, and nonstarters, n = 9) on a control day and 90 min before 4 games played consecutively at home and away venues against local rivals and league leaders. Precompetition psychological states were assessed using the Competitive State Anxiety Inventory−2. The squad recorded 2 wins (home) and 2 losses (away) over the study period. Calculated effect sizes (ESs) showed higher pregame cortisol- (C) and testosterone- (T) difference values before all games than on a baseline control day (ES 0.7−1.5). Similar findings were observed for cognitive and somatic anxiety. Small between-venues C differences were observed in starting XV players (ES 0.2−0.25). Conversely, lower home T- (ES 0.95) and higher away C- (ES 0.6) difference values were observed in nonstarters. Lower T-difference values were apparent in nonstarters (vs starting XV) before home games, providing evidence of a between-groups effect (ES 0.92). Findings show an anticipatory rise in psychophysiological variables before competition. Knowledge of starting status appears a moderating factor in the magnitude of player endocrine response between home and away games.
Steffi L. Colyer, Keith A. Stokes, James L.J. Bilzon, Marco Cardinale, and Aki I.T. Salo
An extensive battery of physical tests is typically employed to evaluate athletic status and/or development, often resulting in a multitude of output variables. The authors aimed to identify independent physical predictors of elite skeleton start performance to overcome the general problem of practitioners employing multiple tests with little knowledge of their predictive utility.
Multiple 2-d testing sessions were undertaken by 13 high-level skeleton athletes across a 24-wk training season and consisted of flexibility, dry-land push-track, sprint, countermovement-jump, and leg-press tests. To reduce the large number of output variables to independent factors, principal-component analysis (PCA) was conducted. The variable most strongly correlated to each component was entered into a stepwise multiple-regression analysis, and K-fold validation assessed model stability.
PCA revealed 3 components underlying the physical variables: sprint ability, lower-limb power, and strength–power characteristics. Three variables that represented these components (unresisted 15-m sprint time, 0-kg jump height, and leg-press force at peak power, respectively) significantly contributed (P < .01) to the prediction (R 2 = .86, 1.52% standard error of estimate) of start performance (15-m sled velocity). Finally, the K-fold validation revealed the model to be stable (predicted vs actual R 2 = .77; 1.97% standard error of estimate).
Only 3 physical-test scores were needed to obtain a valid and stable prediction of skeleton start ability. This method of isolating independent physical variables underlying performance could improve the validity and efficiency of athlete monitoring, potentially benefitting sport scientists, coaches, and athletes alike.